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printcheck_None.log
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printcheck_None.log
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2022-11-19 16:36:23,309 [INFO] SPOTER(
(transformer): Transformer(
(encoder): TransformerEncoder(
(layers): ModuleList(
(0): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=108, out_features=108, bias=True)
)
(linear1): Linear(in_features=108, out_features=2048, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=2048, out_features=108, bias=True)
(norm1): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
)
(1): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=108, out_features=108, bias=True)
)
(linear1): Linear(in_features=108, out_features=2048, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=2048, out_features=108, bias=True)
(norm1): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
)
(2): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=108, out_features=108, bias=True)
)
(linear1): Linear(in_features=108, out_features=2048, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=2048, out_features=108, bias=True)
(norm1): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
)
(3): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=108, out_features=108, bias=True)
)
(linear1): Linear(in_features=108, out_features=2048, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=2048, out_features=108, bias=True)
(norm1): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
)
(4): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=108, out_features=108, bias=True)
)
(linear1): Linear(in_features=108, out_features=2048, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=2048, out_features=108, bias=True)
(norm1): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
)
(5): TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=108, out_features=108, bias=True)
)
(linear1): Linear(in_features=108, out_features=2048, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=2048, out_features=108, bias=True)
(norm1): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
)
)
(norm): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
)
(decoder): TransformerDecoder(
(layers): ModuleList(
(0): SPOTERTransformerDecoderLayer(
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=108, out_features=108, bias=True)
)
(linear1): Linear(in_features=108, out_features=2048, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=2048, out_features=108, bias=True)
(norm1): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
)
(1): SPOTERTransformerDecoderLayer(
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=108, out_features=108, bias=True)
)
(linear1): Linear(in_features=108, out_features=2048, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=2048, out_features=108, bias=True)
(norm1): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
)
(2): SPOTERTransformerDecoderLayer(
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=108, out_features=108, bias=True)
)
(linear1): Linear(in_features=108, out_features=2048, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=2048, out_features=108, bias=True)
(norm1): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
)
(3): SPOTERTransformerDecoderLayer(
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=108, out_features=108, bias=True)
)
(linear1): Linear(in_features=108, out_features=2048, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=2048, out_features=108, bias=True)
(norm1): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
)
(4): SPOTERTransformerDecoderLayer(
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=108, out_features=108, bias=True)
)
(linear1): Linear(in_features=108, out_features=2048, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=2048, out_features=108, bias=True)
(norm1): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
)
(5): SPOTERTransformerDecoderLayer(
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=108, out_features=108, bias=True)
)
(linear1): Linear(in_features=108, out_features=2048, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=2048, out_features=108, bias=True)
(norm1): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(norm3): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
(dropout3): Dropout(p=0.1, inplace=False)
)
)
(norm): LayerNorm((108,), eps=1e-05, elementwise_affine=True)
)
)
(linear_class): Linear(in_features=108, out_features=100, bias=True)
)
2022-11-19 16:36:40,137 [INFO] Starting printcheck...
2022-11-19 16:36:47,543 [INFO] Problematic normalization
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2022-11-19 16:37:54,422 [INFO] [1] TRAIN loss: 4.805483242675104 acc: 0.008321775312066574
2022-11-19 16:37:54,423 [INFO] [1] VALIDATION acc: 0.02967359050445104
2022-11-19 16:37:54,424 [INFO]
2022-11-19 16:37:54,424 [INFO]
Testing checkpointed models starting...
2022-11-19 16:38:00,409 [INFO] Label accuracies statistics:
2022-11-19 16:38:00,409 [INFO] {0: 1.0, 1: 0.5, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0, 13: 0.0, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0, 19: 0.0, 20: 0.0, 21: 0.0, 22: 0.0, 23: 0.0, 24: 0.0, 25: 0.0, 26: 0.0, 27: 0.0, 28: 0.0, 29: 0.0, 30: 0.0, 31: 0.0, 32: 0.0, 33: 0.0, 34: 0.0, 35: 0.0, 36: 0.0, 37: 0.0, 38: 0.0, 39: 0.0, 40: 0.0, 41: 0.0, 42: 0.0, 43: 0.0, 44: 0.0, 45: 0.0, 46: 0.0, 47: 0.0, 48: 0.0, 49: 0.0, 50: 0.0, 51: 0.0, 52: 0.0, 53: 0.0, 54: 0.0, 55: 0.0, 56: 0.0, 57: 0.0, 58: 0.0, 59: 0.0, 60: 0.0, 61: 0.0, 62: 0.0, 63: 0.0, 64: 0.0, 65: 0.0, 66: 0.0, 67: 0.0, 68: 0.0, 69: 0.0, 70: 0.0, 71: 0.0, 72: 0.0, 73: 0.0, 74: 0.0, 75: 0.0, 76: 0.0, 77: 0.0, 78: 0.0, 79: 0.0, 80: 0.0, 81: 0.0, 82: 0.0, 83: 0.0, 84: 0.0, 85: 0.0, 86: 0.0, 87: 0.0, 88: 0.0, 89: 0.0, 90: 0.0, 91: 0.0, 92: 0.0, 93: 0.0, 94: 0.0, 95: 0.0, 96: 0.0, 97: 0.0, 98: 0.0, 99: 0.0}
2022-11-19 16:38:00,411 [INFO] checkpoint_t_0 -> 0.023255813953488372
2022-11-19 16:38:06,701 [INFO] Label accuracies statistics:
2022-11-19 16:38:06,702 [INFO] {0: 1.0, 1: 0.5, 2: 0.0, 3: 0.0, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0, 10: 0.0, 11: 0.0, 12: 0.0, 13: 0.0, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0, 19: 0.0, 20: 0.0, 21: 0.0, 22: 0.0, 23: 0.0, 24: 0.0, 25: 0.0, 26: 0.0, 27: 0.0, 28: 0.0, 29: 0.0, 30: 0.0, 31: 0.0, 32: 0.0, 33: 0.0, 34: 0.0, 35: 0.0, 36: 0.0, 37: 0.0, 38: 0.0, 39: 0.0, 40: 0.0, 41: 0.0, 42: 0.0, 43: 0.0, 44: 0.0, 45: 0.0, 46: 0.0, 47: 0.0, 48: 0.0, 49: 0.0, 50: 0.0, 51: 0.0, 52: 0.0, 53: 0.0, 54: 0.0, 55: 0.0, 56: 0.0, 57: 0.0, 58: 0.0, 59: 0.0, 60: 0.0, 61: 0.0, 62: 0.0, 63: 0.0, 64: 0.0, 65: 0.0, 66: 0.0, 67: 0.0, 68: 0.0, 69: 0.0, 70: 0.0, 71: 0.0, 72: 0.0, 73: 0.0, 74: 0.0, 75: 0.0, 76: 0.0, 77: 0.0, 78: 0.0, 79: 0.0, 80: 0.0, 81: 0.0, 82: 0.0, 83: 0.0, 84: 0.0, 85: 0.0, 86: 0.0, 87: 0.0, 88: 0.0, 89: 0.0, 90: 0.0, 91: 0.0, 92: 0.0, 93: 0.0, 94: 0.0, 95: 0.0, 96: 0.0, 97: 0.0, 98: 0.0, 99: 0.0}
2022-11-19 16:38:06,703 [INFO] checkpoint_v_0 -> 0.023255813953488372
2022-11-19 16:38:06,703 [INFO]
The top result was recorded at 0.023255813953488372 testing accuracy. The best checkpoint is printcheck/checkpoint_t_0.
2022-11-19 16:38:07,073 [INFO]
Any desired statistics have been plotted.
The experiment is finished.